Each new injection molding project has three inherent goals: performance for the customer; production efficiency for the manufacturer; and reliability for the end user. These goals are reasonable. The challenge lies in accomplishing all three within a desired timeframe and budget. To do so, plastics engineers at complex injection molders turn to Design of Experiments (DOE) to identify flaws that would otherwise derail project success.
What is Design of Experiments?
Generally speaking, DOE is one facet of Scientific Molding, a highly precise injection molding practice that improves end results and ROI for complex, critical-use plastic parts and products. Specifically, DOE is a branch of applied statistics that deals with variables used in controlled tests as input and output to determine certain values, like failure probability in injection-molded components.
A DOE matrix takes mathematical and nonmathematical factors into consideration to reach conclusions about performance, production efficiency, and reliability. DOE gathers statistical evidence through multiple measurements and balances them with external influences like timeline and budget to run analysis, construct a mathematical model, and determine next steps based on these findings — either recommended changes or further required testing.
Sample DOE graph
This deeper learning isn’t math for math’s sake. DOE allows engineers to accurately retrace steps and resolve any contradictions that may have arisen.
Performance, Production Efficiency and Reliability
Using Scientific Molding’s DOE approach allows the molder to find the ideal process window, and allows them to see the result of having and maintaining that process window. The mathematical accuracy with which DOE displays the relationships between values and ideal outcomes is fundamental to Scientific Molding on the whole, and also to customer, manufacturer and end user goals for injection molding projects:
DOE has benefits to performance testing because in the DOE process, product and/or process design sensitivities and potential changes are revealed. As a result, adjustments to input values or other standards can be made for correction. From a process standpoint, DOE evaluates areas like materials and injection molding settings like injection speed, melt temperature, cooling time that are sometimes overlooked by product engineers — and raises questions about their influence on design and fulfillment of project specs. This thorough approach to performance testing allows customers to be confident in the plastic component’s performance.
DOE ultimately ensures that poor design is averted prior to manufacture by verifying the process outcome. It also provides a solution for faulty designs that reached the injection molding manufacturing stage, as testing can reveal which changes can be made in manufacturing to accurately and confidently correct missteps. This allows manufacturers to optimize their time and resources at every production step.
DOE is versatile and can be applied to gather data about an injection-molded product’s ability to withstand any number of environmental conditions like temperature, aging, wear, noise and voltage. Data knowledge at this granular level reveals design flaws and potential failure risks, providing ample time for modifications that translate to better, more reliable products that meet or exceed end user expectations.
DOE is an imperative for Scientific Molding, and a mathematically precise way to ensure best outcomes of complex injection molding projects for customers, manufacturers and end users. Learn more in our whitepaper, Scientific Molding: A Molder’s Perspective. Click the button below to download your free copy.